In this work, the decolorization of C.I. Reactive Blue 181 (RB181), an anthraquinone dye, by Ultrasound and Fe(2+) H2O2 processes was investigated. The effects of operating parameters, such as Fe(2+) dosage, H2O2 dosage, pH value, reaction time and temperature were examined. Process optimisation [pH, ferrous ion (Fe(2+)), hydrogen peroxide (H2O2), and reaction time], kinetic studies and their comparison were carried out for both of the processes. The Sono-Fenton process was performed by indirect sonication in an ultrasonic water bath, which was operated at a fixed 35-kHz frequency. The optimum conditions were determined as [Fe(2+)]=30 mg/L, [H2O2]=50 mg/L and pH=3 for the Fenton process and [Fe(2+)]=10 mg/L, [H2O2]=40 mg/L and pH=3 for the Sono-Fenton process. The colour removals were 88% and 93.5% by the Fenton and Sono-Fenton processes, respectively. The highest decolorization was achieved by the Sono-Fenton process because of the production of some oxidising agents as a result of sonication. The paper also discussed kinetic parameters. The decolorization kinetic of RB181 followed pseudo-second-order reaction (Fenton study) and Behnajady kinetics (Sono-Fenton study).

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Phenolic compounds cause significant problems both in drinking water and wastewater due to their toxicity, high oxygen requirements, and low biodegradability. They are listed as primary pollutants by the United States Environmental Protection Agency and the European Union. In this study, the adsorption efficiency of 2,4‐dichlorophenol (2,4‐DCP) on activated carbon, which is commonly used in treatment plants, was investigated under different experimental conditions including adsorbent dose, initial phenol concentration, initial pH, and contact time. As a result of experimental studies, it was determined that the adsorption isotherm and kinetics could be perfectly fitted to Langmuir and the assumption of pseudo‐second order model, respectively. Then, the adaptive neuro‐fuzzy inference system (ANFIS) model was developed, which was the primary purpose of this study. The correlation between training and testing data and the ANFIS output was over 0.999. The generalization ability of the model was found to be 0.999. The input variables such as adsorbent dosage (14.2%), initial concentration (14.6%), initial pH (13.9%), and the contact time (57.2%) showed a higher effect on 2,4‐DCP removal efficiency in the sensitivity analysis. To summarize, modeling studies that are frequently preferred in treatment plants for the removal of different pollutants will reduce the number of experiments harmful to human health and save time, labor, and economy.